@inproceedings{chen-etal-2025-pledgetracker,
    title = "{P}ledge{T}racker: A System for Monitoring the Fulfilment of Pledges",
    author = "Chen, Yulong  and
      Schlichtkrull, Michael Sejr  and
      Deng, Zhenyun  and
      Corney, David  and
      Asl, Nasim  and
      Salisbury, Joshua  and
      Dudfield, Andrew  and
      Vlachos, Andreas",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/lei-li-partial-disambiguation/2025.emnlp-demos.64/",
    pages = "839--850",
    ISBN = "979-8-89176-334-0",
    abstract = "Political pledges reflect candidates' policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic temporal and multi-document nature. To address this issue, we introduce PledgeTracker, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists of three core components: (1) a multi-step evidence retrieval module; (2) a timeline construction module and; (3) a fulfilment filtering module, allowing the capture of the evolving nature of pledge fulfilment and producing interpretable and structured timelines. We evaluate PledgeTracker in collaboration with professional fact-checkers in real-world workflows, demonstrating its effectiveness in retrieving relevant evidence and reducing human verification effort."
}